Senior AI Engineer

Role overview

Senior AI Engineer at Beckman Coulter Diagnostics

In DHR Enterprise AI Solutions, you will be responsible for designing, developing and deploying production-grade AI solutions that create measurable business value across Danaher operating companies.

Responsibilities

• You will work within multidisciplinary engineering teams to transform business opportunities into scalable AI applications, agents, services and reusable capabilities that can be adopted, operated and supported across the enterprise.
• Develop and optimise Large Language Model (LLM) applications using prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, agentic workflows and modern AI engineering techniques.
• Implement and support MLOps, DataOps and LLMOps practices for model lifecycle management, deployment, monitoring, governance and continuous improvement.
• Contribute to the design and implementation of scalable, reliable and maintainable AI architectures, ensuring solutions meet enterprise standards for quality, performance, security and supportability.
• Collaborate closely with Product Owners, Architects, AI Scientists, Platform and Data Engineering teams to build, industrialise and scale trusted, reusable AI solutions across Danaher. 5+ years of software engineering experience with significant hands-on experience building AI, machine learning or advanced analytics solutions.

Preferred qualifications and skills

• It would be a plus if you also possess previous experience in.

Employment details

• Location: Bangalore [Karnātaka] [India]

Benefits

• Building or deploying AI solutions in regulated industries such as life sciences, diagnostics, healthcare or advanced manufacturing.

Additional role details

• Design, develop and deploy AI solutions for enterprise applications and products, building scalable AI services, workflows and reusable capabilities that solve real business problems.
• Strong proficiency in Python and modern AI development frameworks, libraries and tooling.
• Experience developing, deploying and supporting production-grade AI, machine learning or data-driven solutions in cloud environments.
• Strong understanding of software engineering practices, architecture patterns, APIs, microservices, CI/CD and cloud-native development.
• Good understanding of modern AI methodologies, including LLMs, semantic models, knowledge and context graphs, Retrieval-Augmented Generation (RAG) and agentic AI architectures.
• Developing AI capabilities that support knowledge extraction, decision support, process optimisation or workflow automation across enterprise data ecosystems.
• Enterprise AI governance and Responsible AI practices, including model evaluation, explainability, bias assessment and compliance with emerging AI regulations and frameworks.